The Effect of Bias on an Automatically-built Word Sense Corpus
David Martínez, Eneko Agirre · 2004
The goal of this paper is to explore the large-scale automatic acquisition of sense-tagged examples to be used for Word Sense Disambiguation (WSD).We have applied the "monosemous relatives" method on the Web in order to build such a resource for all nouns in WordNet.The analysis of some parameters revealed that the distribution of the word senses (bias) in the training and test corpus is a determinant factor.Provided there is a method to approximate the bias for each word sense, the results we obtained for English are comparable to the use of hand-tagged data (Semcor), which is a very interesting perspective for lesser studied languages.